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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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YOLO-VSF: An Improved YOLO Model by Incorporating Attention Mechanism for Object Detection in Traffic Scenes

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Journal of Shanghai Jiaotong University (science) 2024年 1-14页

作者： Miao, Jun Gong, Shaocui Deng, Yongqiang Liang, Hao Li, Juanjuan Qi, Honggang Zhang, Maoxuan School of Computer Science Beijing Information Science and Technology University Beijing100101 China Beijing VanJee Technology Co. Ltd. Beijing100193 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

Intelligent transportation and autonomous driving systems have made urgent demands on the techniques with high performance on object detection in traffic scenes. This paper proposes an improved object detection model YOLO-VSF over the YOLOv4 model, which is a representative work with excellent performance among YOLO series of object detection models. The main improvement measures include: The backbone feature extraction network CSPDarknet53 of YOLOv4 is replaced with VGG16 to improve the feature extraction capability;SENet attention mechanism is incorporated to improve the salient and correlation feature representation capability;Focal Loss is integrated into the loss function to overcome the sample imbalance problem. In addition, the detection performance of small targets is improved by increasing the resolution of input images. Experimental results show that on the VanJee traffic image dataset provided by Beijing VanJee technology Co., Ltd., the proposed YOLO-VSF model achieves an average mean accuracy (mAP) of 92.21 percentage points, which improves the mAP by 3.04 percentage points compared with the YOLOv4 model while maintaining the detection speed of the original model. On the UA-DETRAC dataset, the average accuracy of YOLO-VSF is close to that of the latest YOLOv7 model with the number of parameters reduced by 1.329 × 107. The proposed method can provide a support for object detection in traffic scenes. © Shanghai Jiao Tong University 2024.

关键词： Object detection

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OPTIMIZING CONSTRUCTION AND DEMOLITION WASTE COLLECTION SYSTEMS THROUGH ADVANCED SCHEDULING

Journal of Solid Waste Technology and Management

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Journal of Solid Waste technology and Management 2024年第2期50卷 555-566页

作者： Ramalakshmi, Shree Smiti HaitherAli, Hammadhu Sivagami, M. Anjali, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Civil Engineering Vellore Institute of Technology Chennai India

Construction and demolition (C&D) waste management is challenging in urban areas due to the high volume of waste generated and widespread illegal dumping. City authorities are struggling with environmental, economic, and social issues due to the lack of a systematic waste management process. An inefficient collection system leads to the loss of valuable urban spaces and clogging of drains with debris. This study aims to develop a simplified algorithm for effective C&D waste collection and transportation systems that maximize efficiency and minimize cost. It analyses various scheduling concepts such as Dijkstra's, Bellman-Ford's, and Floyd-Warshall's algorithm to identify the most efficient route for the collection fleet. The uniqueness of the study lies in providing an integrated solution to the collection and transportation issues of C&D waste management by estimating the volume of debris from photographs and scheduling the collection fleet based on real-world constraints. Waste type, volume, transportation fleet, route and vehicle capacities are considered to aid in efficient resource allocation and utilization. Optimization process involves determining the optimal allocation of waste to the collection fleet, scheduling transportation routes, and assigning human resources. On verification with a real-world case, the model was found to reduce the number of trips and enhance resource utilization, resulting in an efficiency improvement of over 30%. Since waste collection constitutes a significant cost in any solid waste management process, proposed methodology would be beneficial for waste recycling facilities and local waste management authorities to optimize the process, minimize costs, maximize efficiency and reduce environmental footprint. © 2024 Widener University school of Civil Engineering. All rights reserved.

关键词： Debris

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Qualitative and Quantitative Model Checking Against Recurrent Neural Networks

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Journal of computer science & technology 2024年第6期39卷 1292-1311页

作者： Zhen Liang Wan-Wei Liu Fu Song Bai Xue Wen-Jing Yang Ji Wang Zheng-Bin Pang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Institute of Software Chinese Academy of SciencesBeijing 100190China

Recurrent neural networks (RNNs) have been heavily used in applications relying on sequence data such as time series and natural languages. As a matter of fact, their behaviors lack rigorous quality assurance due to the black-box nature of deep learning. It is an urgent and challenging task to formally reason about the behaviors of RNNs. To this end, we first present an extension of linear-time temporal logic to reason about properties with respect to RNNs, such as local robustness, reachability, and some temporal properties. Based on the proposed logic, we formalize the verification obligation as a Hoare-like triple, from both qualitative and quantitative perspectives. The former concerns whether all the outputs resulting from the inputs fulfilling the pre-condition satisfy the post-condition, whereas the latter is to compute the probability that the post-condition is satisfied on the premise that the inputs fulfill the pre-condition. To tackle these problems, we develop a systematic verification framework, mainly based on polyhedron propagation, dimension-preserving abstraction, and the Monte Carlo sampling. We also implement our algorithm with a prototype tool and conduct experiments to demonstrate its feasibility and efficiency.

关键词： recurrent neural network model checking temporal logic qualitative/quantitative verification

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A Risk Prediction Model Based on Crash History Data for Railway Trams

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Chinese Journal of Electronics 2023年第5期32卷 963-971页

作者： JI Wenjiang YANG Jiangcheng WANG Yichuan ZHU Lei QIU Yuan HEI Xinhong School of Computer Science and Engineering Xi'an University of Technology

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.

关键词： Solid modeling Clustering algorithms Feature extraction Prediction algorithms Rail transportation Safety History

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1421-1434页

作者： Yousheng ZHOU Zhonghan WANG Yuanni LIU School of Computer Science and Technology Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications

As a new computing method, edge computing not only improves the computing efficiency and processing power of data, but also reduces the transmission delay of data. Due to the wide variety of edge devices and the increasing amount of terminal data, third-party data centers are unable to ensure no user privacy data leaked. To solve these problems, this paper proposes an iterative clustering algorithm named local differential privacy iterative aggregation(LDPIA) based on localized differential privacy, which implements local differential privacy. To address the problem of uncertainty in numerical types of mixed data, random perturbation is applied to the user data at the attribute category level. The server then performs clustering on the perturbed data, and density threshold and disturbance probability are introduced to update the cluster point set iteratively. In addition, a new distance calculation formula is defined in combination with attribute weights to ensure the availability of data. The experimental results show that LDPIA algorithm achieves better privacy protection and availability simultaneously.

关键词： Differential privacy Privacy Uncertainty Perturbation methods Clustering algorithms Wheels Iterative algorithms Servers Protection Edge computing

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Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

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computers, Materials & Continua 2023年第5期75卷 4377-4393页

作者： Chunzhi Wang Weidong Cao Yalin Hu Jinhang Liu School of Computer Science Hubei University of TechnologyWuhan430068China

According to Cisco’s Internet Report 2020 white paper,there will be 29.3 billion connected devices worldwide by 2023,up from 18.4 billion in 2018.5G connections will generate nearly three times more traffic than 4G *** bringing a boom to the network,it also presents unprecedented challenges in terms of flow forwarding *** path assignment mechanism used in traditional traffic schedulingmethods tends to cause local network congestion caused by the concentration of elephant flows,resulting in unbalanced network load and degraded quality of *** the centralized control of software-defined networks,this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing(MCQG).The ideal transmission path is selected for data flows while considering the network congestion rate and quality of *** traffic scheduling strategies are used according to the characteristics of different service types in data *** scheduling for elephant flows that tend to cause local *** path evaluation function is formed by the maximum link utilization on the path,the number of elephant flows and the time delay,and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant *** is used to reduce the possibility of local network congestion *** cost multi-path(ECMP)protocols with faster response time are used to schedulemouse flows with shorter *** to guarantee the quality of service of the *** achieve isolated transmission of various types of data *** experimental results show that the proposed strategy has higher throughput,better network load balancing,and better robustness compared to ECMP under different traffic *** addition,because it can fully utilize the resources in the network,MCQG also outperforms another traffic s

关键词： Software-defined network data center network OpenFlow network congestion quality of service

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UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

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Digital Communications and Networks 2024年第4期10卷 837-852页

作者： Jing Bai Jinsong Gui Guosheng Huang Shaobo Zhang Anfeng Liu School of Computer Science and Engineering Central South UniversityChangSha410083China School of Information Science and Engineering Hunan First Normal UniversityChangsha410205China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangTan411100China

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the *** this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for *** main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data ***,we conducted extensive experiments to evaluate the UAV-ITD *** results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

关键词： Unmanned aerial systems Trust computing Truth discovery Deep matrix factorization Low-cost communications

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Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

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computer Modeling in Engineering & sciences 2024年第11期141卷 1827-1846页

作者： Xinchang Zhang Xiaokang Pan Rongjie Zhu Runda Guan Zhongfeng Qiu Biao Song Organization Department Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Teacher Education Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics&Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

关键词： Mesoscale eddy detection object detection contour detection remote sensing

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